Lukas Sigrist

Mixed-criticality runtime mechanisms and evaluation on multicores

Multicore systems are being increasingly used for embedded system deployments, even in safety-critical domains. Co-hosting applications of different criticality levels in the same platform requires sufficient isolation among them, which has given rise to the mixed-criticality scheduling problem and several recently proposed policies. Such policies typically employ runtime mechanisms to monitor task execution, detect exceptional events like task overruns, and react by switching scheduling mode. Implementing such mechanisms efficiently is crucial for any scheduler to detect runtime events and react in a timely manner, without compromising the systems safety. This paper investigates implementation alternatives for these mechanisms and empirically evaluates the effect of their runtime overhead on the schedulability of mixed-criticality applications. Specifically, we implement in user-space two state-of-the-art scheduling policies: the flexible time-triggered FTTS [1] and the partitioned EDFVD [2], and measure their runtime overheads on a 60-core Intel R Xeon Phi and a 4-core Intel R Core i5 for the first time. Based on extensive executions of synthetic task sets and an industrial avionic application, we show that these overheads cannot be neglected, esp. on massively multicore architectures, where they can incur a schedulability loss up to 97%. Evaluating runtime mechanisms early in the design phase and integrating their overheads into schedulability analysis seem therefore inevitable steps in the design of mixed-criticality systems. The need for verifiably bounded overheads motivates the development of novel timing-predictable architectures and runtime environments specifically targeted for mixed-criticality applications.

Dynamic energy burst scaling for transiently powered systems

Energy harvesting is generally seen to be the key to power cyber-physical systems in a low-cost, long term, efficient manner. However, harvesting has traditionally been coupled with large energy storage devices to mitigate the effects of the sources variability. The emerging class of transiently powered systems avoids this issue by performing computation only as a function of the harvested energy, minimizing the obtrusive and expensive storage element. In this work, we present an efficient Energy Management Unit (EMU) to supply generic loads when the average harvested power is much smaller than required for sustained system operation. By building up charge to a pre-defined energy level, the EMU can generate short energy bursts predictably, even under variable harvesting conditions. Furthermore, we propose a dynamic energy burst scaling (DEBS) technique to adjust these bursts to the loads requirements. Using a simple interface, the load can dynamically configure the EMU to supply small bursts of energy at its optimal power point, independent from the harvesters operating point. Extensive theoretical and experimental data demonstrate the high energy efficiency of our approach, reaching up to 73.6% even when harvesting only 110 μW to supply a load of 3.89mW.

Measurement and validation of energy harvesting IoT devices

With the appearance of wearable devices and the IoT, energy harvesting nodes are becoming more and more important. The design and evaluation of these small standalone sensors and actuators, which harvest limited amounts of energy, requires novel tools and methods. Fast and accurate measurement systems are required to capture the rapidly changing harvesting scenarios and characterize leakage currents and energy efficiencies. The need for real-world experiments creates a demand for compact and portable equipment to perform in-situ power measurements and environmental logging. This work presents the RocketLogger, a hand-held measurement device that combines both properties: portability and accuracy. The custom analog front-end allows logging at sampling rates up to 64 kSPS. The fast range switching within 1.4 μ8 guarantees continuous power measurements starting from 4pW at 1 mV up to 2.75 W at 5.5 V. The software provides remote control and manages data acquisition of up to 13Mb/ sec in real-time. We extensively characterize the RocketLoggers performance, demonstrate the need for its properties in three use-cases at different stages of the system design flow, and show its advantages in measuring and validating new harvesting-driven devices for the IoT.

Wearable, energy-opportunistic vision sensing for walking speed estimation

State-of-the-art wearable systems are typically performance-constrained, battery-based devices which can, at most, reach self-sustainability using energy harvesting and aggressive duty-cycling. In this work, we present a wearable vision sensor node which can reliably execute computationally-intensive computer-vision algorithms in an energy-opportunistic fashion. By leveraging a burst-generation scheme, the proposed system can efficiently provide the energy guarantees required for tasks with temporal dependencies, even under highly variable harvesting conditions. By mounting the node on a users glasses, the node is able to acquire a sequence of images and determine the users walking speed, requiring only a small solar panel and capacitor. Both hardware and software have been fully optimized for ultra-low power consumption and high performance. Extensive experimental results show the energy nodes energy proportionality and the accuracy of its walking speed estimation.

Efficient, Long-Term Logging of Rich Data Sensors Using Transient Sensor Nodes

While energy harvesting is generally seen to be the key to power cyber-physical systems in a low-cost, long-term, efficient manner, it has generally required large energy storage devices to mitigate the effects of the source’s variability. The emerging class of transiently powered systems embrace this variability by performing computation in proportion to the energy harvested, thereby minimizing the obtrusive and expensive storage element. By using an efficient Energy Management Unit (EMU), small bursts of energy can be buffered in an optimally sized capacitor and used to supply generic loads, even when the average harvested power is only a fraction of that required for sustained system operation. Dynamic Energy Burst Scaling (DEBS) can be used by the load to dynamically configure the EMU to supply small bursts of energy at its optimal power point, independent from the harvester’s operating point. Parameters like the maximum burst size, the solar panel’s area, as well as the use of energy-efficient Non-Volatile Memory Hierarchy (NVMH) can have a significant impact on the transient system’s characteristics such as the wake-up time and the amount of work that can be done per unit of energy. Experimental data from a solar-powered, long-term autonomous image acquisition application show that, regardless of its configuration, the EMU can supply energy bursts to a 43.4mW load with efficiencies of up to 79.7% and can work with input power levels as low as 140μW. When the EMU is configured to use DEBS and NVMH, the total energy cost of acquiring, processing and storing an image can be reduced by 77.8%, at the price of increasing the energy buffer size by 65%.

RocketLogger: Mobile Power Logger for Prototyping IoT Devices: Demo Abstract

We demonstrate the RocketLogger, a mobile data logger designed for prototyping energy harvesting IoT devices. Novel IoT applications require new dataloggers with a highly increased dynamic range for current measurement to accommodate both ultra-low sleep currents of few nanoamperes as well as wireless communication currents in the range of hundreds of milliamperes. In parallel to ultra-low currents and high dynamic range measurements, novel applications require mobile measurements for easy in-situ characterization or wearable device testing. The RocketLogger is a solution that fulfills these requirements. While being fully mobile, it measures currents from 5 nA up to 500 mA with very fast and seamless range-switching. Using a sample energy harvesting application, we demonstrate its low-current measurement capabilities, fast, seamless auto-ranging and easy-to-use remote user interface.